Adaptive demodulation method and apparatus using an artificial neural network to improve data recovery in high speed channels

ABSTRACT

A neural network demodulator is used within a receiver to provide Inter Symbol Interference (ISI) channel equalization and to correct for I/Q/phase imbalance. The neural network is trained with a single integrated training step to simultaneously handle the channel impairments of ISI equalization and I/Q phase imbalance as opposed to prior art methods of separately addressing each channel impairment in sequence.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/312,072, filed Jun. 23, 2014, hereby incorporated herein byreference, and U.S. Provisional Patent Application No. 61/837,742, filedJun. 21, 2013.

FIELD OF THE INVENTION

The present invention relates to a neural network based integrateddemodulator that mitigates channel impairments, ISI and I/Q channelleakages, and minimizes the impact on the overall performance of thesystem. In this process, the inventive demodulator improves theperformance for data recovery when operating at high data rates over thetransmission channel.

BACKGROUND OF THE INVENTION

High-Speed communications systems typically use a wide band channelwhere the transmission is achieved using Radio Frequency (RF) carriers.RF transmission as it exists today either uses closely spaced narrowband multiple carriers or a small number of carriers containing basebandmodulated signals.

An example of a closely spaced narrow band multiple carrier system isthe Orthogonal Frequency Division Multiplexing (OFDM) system, which usesa large number of RF carriers with each carrier carrying two base bandmodulation signals (I/Q). Since OFDM uses orthogonal carriers, thetransmission does not suffer any Inter-Frequency-Interference (IFI).Also, the data processing at the receiver uses a simple Fast FourierTransform (FFT) technique. Due to the orthogonality of the RF carriers,an OFDM transmission system is more robust to Inter-Symbol-Interference(ISI). However, when ISI exists, the system requires the use of a CyclicPrefix as an overhead and a channel equalizer to handle ISI.

An example of a small number of carriers containing baseband modulatedsignals is the “Kelquan” system based on the teachings presented in U.S.Patent Nos. 5,956,372 and 8,233,564 where closely spaced non-orthogonalfrequencies are used to create baseband modulated signals which arecarried in a small number of RF carriers as I/Q channels over a widebandbandwidth. In this system, the data is recovered optimally after the IFIsuppression using a Neural Network Matched Filter. This system requiresno overhead, but needs a robust equalizer to handle ISI.

In both of the above scenarios, the performance of high-speed digitaltransmission suffers high degradation due to the effects of channelimpairments. Specifically, the channel impairments, which includeInter-Symbol-Interference and the leakage of I/Q modulated signals,which are sent over each of the RF carriers, significantly degrades theBit Error Rate (BER) performance. The ISI is caused by the change ofbandwidth of the frequencies of specific symbols, spilling over to thenext set of symbols, or to the previous set of symbols. The leakage ofI/Q signals on each other is caused by the imperfect phase alignmentbetween the transmit and receiver carrier phases. In OFDM systems, theleakage of I/Q signals can be more predominant in wireless channels asopposed to wireline channels. In a small number of carrier basedsystems, both wireline and wireless channels experience leakage of I/Qdue to imperfect phase imbalance. As the transmit systems carry largedata rates, the sensitivity to these channel impairments becomesignificant.

In accordance with the invention described herein, an Artificial NeuralNetwork (ANN) based demodulator is shown that handles the ISI and I/Qleakage due to phase imbalance as a single apparatus. The novel designof this demodulator simplifies the adaptive demodulator complexity andimproves the data recovery process significantly in terms of Bit ErrorRates (BER).

While the invention is applicable to broader transmission channels, thepreferred embodiment is a system that has a small number of RF carriersfor transmission over a wideband channel.

Traditional systems use two different systems to handle these twochannel impairments, where each system requires separate training timeduring initialization. When both impairments are handled separately witheach requiring its own training time, the computation time to optimizethe design with appropriate correction coefficients increases. Also,there could be bottlenecks in the design process to achieve optimalsystem performance, when these two impairments are handled sequentially,one after another. The teaching of this invention is directed to anintegrated demodulator that avoids this pitfall by simultaneouslyhandling both I/Q imbalance and ISI with a single training sequence.This process develops the necessary coefficients for an ANN demodulatorto achieve optimum performance.

In summary, this invention teaches the design of an Artificial NeuralNetwork based Demodulator that achieves the following functions at thereceiver:

1. Compensates for the I/Q imbalance due to carrier phase miss-alignmentbetween the transmitter and receiver

2. Equalizes the ISI introduced by the channel

3. Equalizes the ISI introduced by the channel filter 4. Recovers theoriginal data which was used for modulation at the sending side.

The proposed invention achieves significant advantages over traditionalmethods of handling transmission impairments, for example,

It reduces the computational complexity of the demodulation processusing single operation as opposed to multiple operations.

It leads to more accurate and robust handling of channel impairments atthe receiver due to integrated operation instead of sequentialoperations.

It increases the battery life of mobile apparatus (particularly usefulto handheld devices) by extending the mean time before failure.

Equalization techniques broadly support handling transmissionimpairments over different channels: wireline communications or wirelesscommunications or highly dispersive channels. The transmissionimpairments can be different in different channels.

In wireline channels, the channel equalization is designed to handle ISIand reflections. The concept of equalization relates to the losscompensation for the equalizer as a figure of merit, which is used toderive the performance of the data recovery at the receiver. Since thedistance between the sending side and the receiving side is fixed, thechannel characteristics are known ‘a priori’ and it is possible to a usea Minimum Mean Squared Error (MMSE) equalizer to minimize the effect ofISI. When the channel transfer function is unknown, it is imperative touse an adaptive MMSE equalizer.

There are implementations of equalizers used to handle ISI based onLeast Mean Squared Error (LMSE). This equalizer performs well inminimizing the effect of ISI as long as the phase variation on thechannel is low. Although a LMSE equalizer works well in a minimum phasechannel, its performance is very limited in a channel with spectralnulls. In such cases, the convergence of an LMS equalizer is notguaranteed and ISI effects cannot be minimized.

Another alternative to handle the ISI problem is the use of a DecisionFeedback Equalizer (DFE). While the DFE outperforms the LMS, it is morecomplex than the LMS equalizer. Furthermore the DFE suffers from anerror propagation problem and therefore is only used at very high SNRscenario. The MMSE, LMSE and DFE equalizers can only minimize the effectof ISI on the performance, but cannot handle the I/Q phase alignmentproblem. The present invention of an integrated demodulator which bothequalizes ISI and compensates I/Q/imbalance outperforms a LMSE equalizereven for non-minimum phase variation in the channel.

In wireless channels, the channel equalization is more complex whenhandling ISI due to rapid changes in channel behavior because ofmobility and channel fading. The channel can be modeled as a highlydispersive channel and will require a more complex operation to reduceor eliminate the ISI effects. These channels tend to be more timeinvariant, but are adaptive and therefore, the channel equalizers tendto be adaptive to compensate and adjust for the slow variations of thechannel.

Some of the equalization methods used to handle wireless channelsinclude:

a. The method to nullify or mitigate the effect of channel response byemploying a training period to initialize the channel equalizer that hasa simple adaptive system. Some techniques in this category include alsoa blind equalization technique without a training period by employingdifferent and possible-to-estimate channel characteristics.

b. For OFDM channels, which use a narrow frequency band, the channelequalization reduces the problem to handle flat fading or a frequencynon-selective system.

c. For handling channel fading, techniques such as multiple transmissionof the same information over independent channels and waiting for thefading to recede before sending have been exploited. The ultimatemeasure is the improvement of probability of error in fading channels.

In summary, there are many teachings to design channel equalizers basedon neural networks to handle one selective parameter at a time. As such,designing the equalizer to handle multiple effects on the channel ismore optimum and robust than handling parameters one at a time which cancause delay in processing to achieve optimization.

The proposed teaching in this invention is to demonstrate designing theinventive demodulator to handle the effects of more than one parametersimultaneously with a single training sequence while achieving optimumperformance for data recovery at the receiver.

SUMMARY OF THE INVENTION

The invention described herein is directed to a neural network baseddemodulator for use in a communication system, wherein information sentover the communication channel can be impaired by I/Q/imbalance andInter Symbol Interference. The neural network based demodulatorfunctions to simultaneously compensate for the I/Q imbalance and toequalize the Inter Symbol Interference after a single integratedtraining step.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block schematic diagram of a high-speed data transmissionsystem.

FIG. 2 is an illustration of TXSRF using LO exponent of 4096 and RXSRFwith LO exponent of 1.

FIG. 3 is a schematic of a neural network performing I/Q balancing andequalization simultaneously.

DETAILED DESCRIPTION

The objective of the present invention is to provide a design for anArtificial Neural Network (ANN) based Demodulator that consists of thefollowing elements which operate as an integrated process for ISIhandling and I/Q channel phase imbalance: Sending side:

TXSRF (Pulse Shaper) Receiving side:

I/Q Demodulator

RXSRF (Pulse Matching)

ANN Demodulator with I/Q Balancer

ANN Equalizer

The preferred embodiment for the present invention is an ANN Demodulatorfor use in a system such as described in U.S. Pat. Nos. 5,956,372 and8,233,564, although the use of the present invention is not limited tothis preferred embodiment.

FIG. 1 shows the overall block schematic of the system for high-speeddata transmission that includes the components of the ANN basedDemodulator.

(1) TXSRF as a Pulse Shaper

The TXSRF which is part of the earlier patents (U.S. Pat. Nos. 5,956,372and 8,233,564) as well as this application's parent, takes the PulseAmplitude Modulated sinusoids and creates spike voltages. In thereferenced patents and patent application, the spike voltages arecreated by having very high re-circulation for each symbol in the orderof a million samples per second. From a practical implementation, there-circulation rate is kept at 64 mega samples per second while theLocal Oscillator has an exponent of 4096. The TXSRF which creates thespikes is referred to as a “Pulse Shaper” (see FIG. 1). FIG. 2illustrates the modified TXSRF with LO exponent of 4096. Exponentimplies that the LO output is raised to the power of 4096. Since theimplementation of TXSRF can be done using a look up table with allpossible inputs and all possible outputs, an LO exponent of thismagnitude can be implemented more realistically.

The output of TXSRF goes through a Transmit Band Pass Filter (TXBPF) asshown in FIGS. 1 and 2.

(2) I/O Demodulator

Each RF carrier has two independent baseband data channels which areorthogonal to each other (I and Q). They can be separated and thedemodulator extracts the baseband signals before being processed by anindividual RXSRF. The I and Q demodulated signals are passed throughRXBPF before processing by RXSRF.

(3) RXSRF (Pulse Matching)

The RXSRF is already shown in earlier U.S. Pat. Nos. 5,956,372 and8,233,564, as well as this application's parent. It is critical that theLO exponent is not increased in the RXSRF. The input to the RXSRF is acombination of multiple signals from different TXSRFs with added channelnoise and when the exponent of LO is raised, even though the signalsspike, the channel noise can exacerbate and cause degradation in the BERwhile recovering the data. Therefore, the LO exponent of RXSRF is keptat 1 as shown in FIG. 2.

(4) ANN Demodulator with I/Q Balancer

The performance of traditional digital modulation schemes operating athigh data rates suffers significant degradation due to the effects ofchannel impairment. The important channel effects include Inter SymbolInterference (ISI), and I/Q channel leakage which is due to imperfectphase alignment between transmit and receive carriers.

In the prior art, these two channel impairments are handled separately,each requiring its own training time and computation time to come upwith appropriate correction coefficients.

For high data rate operation, this can create a bottleneck resulting inpoorer performance when each an impairment is handled before the other.For example, when I/Q phase balancing is achieved before handling ISI byusing an equalizer, the equalizer may not operate optimally with respectto performance errors. On the other hand, if the equalizer is designedoptimally, I/Q balancing may fail resulting in higher performanceerrors.

The present invention teaches simultaneous handling of both theimpairments as part of the inventive ANN demodulator to achieve optimalBER performance. The ANN demodulator handles the following operations atthe receiver:

1. Compensates for the I/Q imbalance due to carrier phase miss-alignmentbetween the transmitter and receiver

2. Equalizes the ISI introduced by the channel

3. Equalizes the ISI introduced by the channel filter

4. Recovers the original modulated symbols for recovering the data.

The proposed invention uses the same training sequence for handling theimpairments simultaneously.

This invention simultaneously handles both I&Q balancing and ISI withthe same training sequence. The teachings of the design of the neuralnetwork and the training algorithm for matched filter application ispresented in patent application U.S. Ser. No. 14/312,072, which is theparent of this application. The proposed invention extends the teachingsof U.S. Ser. No. 14/312,072 on the ANN match filter described therein toa combined matched filtering, equalization and I/Q balancing algorithm.

This invention reduces the training time required for the digital signalprocessing of the algorithms for match filtering, equalization and I/Qbalancing. The training time taken for the integrated process issignificantly lower compared to processing each of these algorithmsindependently. In addition, the computational complexity of thedemodulation process which is a combination of multiple processesreduces to a single operation. This reduction in complexity willincrease the battery life of a hand held device and extend the mean timebefore failure of the device. In more general terms, this invention willincrease the versatility and agility of a digital communicationreceiver. Also it leads to a more robust handling of channel impairmentsby the receivers.

(5) ANN Demodulator

The demodulator is a combined I/Q balancer and equalizer i.e. it has thecapability of handling both interference and ISI cancellation.

Assuming that a complex modulated data stream at the transmitter isgiven by

x ^(t) =x _(I) ^(t) +jx _(Q) ^(t)  (1)

After passing through a complex channel with channel matrix given by

H=H _(I) +jH _(Q)  (2)

The channel output Y is given as the product of X and H yielding

y=Hx ^(t) +n=y _(I) +jy _(Q) +n=H _(I) x _(I) ^(t) +jH _(Q) ^(t) +n  (3)

Where n is the noise vector.

When the orthogonality of I and Q is lost due to imperfect phasesynchronization, the new received signal becomes a linear combination ofthe I/Q components, i.e.

y′=y′ _(I) +jy′ _(Q) +n  (4)

Where

y′ _(I) =ay _(I) −by _(Q)

y′ _(Q) =by _(I) +ay _(Q)  (5)

With the parameters a=cosθ and b=sinθ, and θ is the phase angledifference between transmit carrier and the reference LO carrier.

In traditional systems, the I/Q imbalance is first taken care of beforethe channel equalization. This could be achieved in two ways:

1. Using carrier training: During the training period, an unmodulatedcarrier is sent from the transmitter to the receiver. Based on thereceived signal at the output of the matched filters, θ could bedetermined using equation (5) and the receiver LO phase can be adjustedaccordingly to make sure that θ becomes zero thereby isolating I from Qchannels. This method is used when the channel is (quasi) stationary.

2. Using real time phase recovery: Alternatively, when the channelvaries more frequently, y_(I) and y_(Q) can be extracted from y′_(I) andy′_(Q) without explicitly obtaining the phase difference θ. This methodcomes with an additional computational complexity during the datarecovery stage.

After the I/Q channels are balanced, each of the recovered I and Q data(y_(I) and y_(Q)) are then equalized independently to recover theoriginally transmitted baseband modulated symbols x_(I) ^(t) and x_(Q)^(t). Since H is not known a priori, there's another training sequencerequired in order to estimate the channel impulse response and thendetermine the equalizer coefficient. The computed equalizer coefficientscan then be used to recover x_(I) ^(t) and x_(Q) ^(t).

The overall computational complexity by the above method introduces morereal estate in the hardware as well as adds more computational time.

In contrast, in the proposed teaching, the inventive demodulatordescribed herein uses only a single training period, where the equalizerfunction iteratively computes efficient coefficients that can be used tobalance I/Q channels and perform equalization at the same time. Duringthe training stage, both I/Q data are fed into the neural network asinputs (shown in FIG. 3). The neural network shown in FIG. 3 is the sametwo layer configuration shown in FIG. 5 of parent application Ser. No.14/312,072. The neural network will then determine the coefficients tocreate an appropriate model that performs I/Q balancing and equalizationat the same time. Since the computational complexity of training asingle neural network is similar to that of training a traditionalequalizer, the second stage training process has now been reduced into asingle one. Also, only one neural network equalizer is required for bothI/Q channels.

As set forth in parent application Ser. No. 14/312,072, the neuralnetwork training process determines the coefficients w1, b1, w2, b2 suchthat the mean squared error between the transmitted symbols x^(t) andthe equalized symbols x^(r) (where x^(r)=x_(I) ^(r)+jx_(Q) ^(r) is theoutput of the neural network demodulator is minimized. That is, theoptimization problem can be defined as:

$\begin{matrix}{\begin{matrix}{argmin} \\{{w\; 1},{b\; 1},{w\; 2},{b\; 2}}\end{matrix}{E\left\lbrack {\left( {x^{r} - x^{t}} \right)}^{2} \right\rbrack}} & (5)\end{matrix}$

The neural network output is calculated from the following mathematicalsteps: The neural network input is the vector concatenation of thereceived I & Q data, i.e.

P=[y′ _(t) ; y′ _(Q)]  (6)

N=w1×P+b1  (7)

A1=TF(N)=max(−1, min(1, N))  (8)

A2=w2×A1+b2  (9)

x ^(r)=A2[x_(I) ^(r)]+j A2[x_(Q) ^(r)]  (10)

Where A2[z] is the subset of A2 that correspond to the z.

It is worth mentioning that the neural network input signal could besampled at the symbol rate and therefore the neural network equalizerwill function similar to a symbol-by-symbol equalizer or the input couldbe oversampled giving rise to a fractionally spaced equalizer. In thecase where input sampling rate is the same as the sampling rate at thereceiver input, the neural network demodulator can flexibly add matchedfiltering to its functions. In fact it is the ability of the neuralnetwork to combine matched filtering, I/Q balancing and equalizationthat lead to the name “neural network demodulator”.

The following section compares the neural network equalizer function tothe traditional transversal (LSM/RLS) equalizer:

The neural network equalizer can be seen as a two stage transversalequalizer. Therefore the residual ISI from the first layer is canceledin the second layer which makes it more superior to traditionaltransversal equalizers.

The neural network equalizer can be trained with varying length of inputvector to speed up its convergence depending on how fast the channel isvarying whereas as traditional transversal equalizer can only train oneinput sample at a time. In fact, due to the stochastic nature of theinput vector, traditional transversal equalizers may never converge orbe able to capture the underlying ISI signature of the channel.

Also, the neural network equalizer contains a transfer function TF(N)between the two layers to ensure that the training converges and doesnot diverge or get stuck in a sub-optimal local minimal

The proposed invention uniquely exploits the flexibility that the neuralnetwork offers to handle multiple tasks faster which has never beenexploited in traditional equalizers as designed for use in digitalcommunication systems.

Although the present invention has been described in conjunction withspecific embodiments, those skilled in the art of the present inventionwill appreciate that modifications and variations can be made withoutdeparting from the scope and the spirit of this invention. Suchmodifications and variations are envisioned to be within the scope ofthe amended claims.

1. A method for use in a communication system, the communication systemincluding a transmitter and a receiver, to provide ISI channelequalization and to correct I/Q phase imbalance, comprising, providing ademodulator as part of the receiver, the demodulator including atrainable neural network, and training the neural network in a singleintegrated training step to simultaneously enable the demodulator toprovide ISI channel equalization and to correct I/Q phase imbalance. 2.A method in accordance with claim 1 wherein the neural network includestwo layers, the method further comprising the step of implementing atransfer function between the two layers of the neural network to ensureconvergence of the neural network training step.
 3. A method inaccordance with claim 2 wherein transmitter data symbols and RF carriersignals are sent over the communication channel from the transmitter tothe receiver with the I/Q imbalance resulting from carrier phasemisalignment between the transmitter and receiver, said neural networktraining step utilizing information sent over the communication channelto determine coefficients needed by the neural network to correct saidcarrier phase misalignment.
 4. A method in accordance with claim 3wherein transmitter data symbols and RF carrier signals are sent overthe communication channel from the transmitter to the receiver with ISIresulting by the change of bandwidth of the carrier signal frequencycausing interference between adjacent transmitter data symbols, saidneural network training step utilizing information sent over thecommunication channel to determine coefficients needed by the neuralnetwork to prevent the interference between transmitter data symbols. 5.A method in accordance with claim 4 wherein the neural network trainingprocess determines coefficients W1, b1 W2 and b2 such that the meansquared error between a transmitted symbol and a symbol received at theoutput of the demodulator is minimized.
 6. A communications system,comprising, a transmitter for sending modulated data symbols and carriersignals over the communications channel, a receiver including ademodulator, and a trainable neural network included as part of thedemodulator, which when trained in a single integrated training stepallows the demodulator to simultaneously provide ISI channelequalization and correct I/Q phase imbalance.
 7. A communications systemin accordance with claim 6, wherein the neural network includes twolayers, one hidden layer and one output layer, the hidden layer applyinga first set coefficients to data symbols from the receiver to create afirst set of fixed point words, and processing the first set of fixedpoint words with a transfer function.
 8. A communications system inaccordance with claim 7, wherein the first set of fixed point words aresent to the output layer after processing by the transfer function andapplied to a second set of coefficients.
 9. A communications system inaccordance with claim 8, wherein the neural network is trained based oninformation sent over the communication channel to generate the firstand second set of coefficients.
 10. A communications system inaccordance with claim 9, wherein the first and second set ofcoefficients ensure that the mean squared error between a transmitteddata symbol and a symbol at the output of the demodulator is minimized.